Dive into Data Analytics!

Hi! I’m Aaron, Data Analyst/ Scientist/ Janitor/ Artist/ Magician at Wego.com, an online travel metasearch. I created this blog to share about data, analytics, and the web.

Wego.com is headquartered in Singapore, with a huge presence in the Middle East. This is a story on why I started this blog, and how I got started in data analytics. A word of caution in advance, this page is a little long, littered with lots of unnecessary details.

Why

Data analytics, while not being a totally new field, has been gaining popularity in recent times. Having been in data analytics as a profession for a short while by now (since 2015), I think there might be a thing or two I can share about the art and science of the industry and profession that may be helpful to anyone contemplating a move into data analytics, or anyone hoping to gain an understanding of data analytics, be it as a hobby, or as part of your career. By the way things are going, an understanding data analytics will soon become an integral part of any job.

Along the way, friends have asked about the field, what I do, and how to learn more about it. If you ever find yourself in a similar situation, this site is for you! You’ll be able to get a better understanding of what data analytics involve, and how you can get involved. While I had a unique path to the world of data analytics, I’ve learnt that the barriers to entry to this industry is not high. With some effort, you’ll be able to leverage on the growing amount of data collected globally to make an impact.

If you are already in data analytics, this site is definitely for you too! Beyond the basics, I’ll be sharing on latest ideas I’m working on. I hope you’ll find these ideas useful to your work/ passion. Feel free to share your thoughts on whether these ideas make sense to you or not! I’d love to improve on them and share your contributions with this community 🙂

Where Did I Begin

The Offer

While on my grad trip alone, back in 2015, I received an offer to join the Google Squared Data and Analytics Program. It came as a surprise, as after months of job applications, I had figured that I was an ‘undesirable candidate’. I landed just 2 or 3 interview invites (that did not proceed further) and 1 job offer (from a company many of my peers were avoiding, but as I did not have a choice then, accepted it). The program, on the other hand, was something that sounded too good to be true, a doorway into the up and coming field of data analytics, no experience required, learning from the best at Google and NUS, and even an industry placement! Of course I grabbed it.

Fortunately, I had not bought my flight home then, and cut short my Singapore to Europe overland trip at Moscow, returning home just a day before the program started.

The Challenge

I’d elaborate on the details of things I did and learnt in the program and at Wego in separate post, but here’s all of it in a nutshell. The program provided a good understanding on the breadth and width of the industry. On the other hand, the internship at Wego (before it became full time) allowed me to learn and practise technical skills required in the field. I was able to leverage on my prior (limited) knowledge on business and accountancy, but still much more to learn. With the support of my manager/mentor and lots of online resources, I managed to overcome the steep learning curve.

The Stuff I Work On Everyday

Our first primary set up (in 2015) relied heavily on BigQuery, Google Sheets and Google App Scripts. There was also a high reliance on our backend developers to deliver insights and reports to our partners. As we progressed, our next primary set up was built on BigQuery, Python, and Google Compute Engine. We were (and still are) a small team (none having a computer science background), but we got through the transition. Things were much more scalable and reliable with the new set up, and we were less reliant on our developers, who were already packed with other projects. We kept some of our projects on the old set up, and I’d explore why in a later post. There’re pros and cons of each approach. Throughout, we used Chartio as our visualisation platform across the organization.

A Sample of projects

As with most small/young data teams, we spend ~80% of our time building infrastructure, cleaning data, and identifying anomalies. Here are some examples of such projects I’ve worked on personally:

Automating reporting and insight generation for our partners

Formulating identification of anomalies and automating alerts

Integrating third party data sources into our database

Building tools that allow everyone across the organisation to leverage on our data resources to make timely, informed, decisions. Regardless of technical ability

Another good ~20% of our time is spent analysing data. With sufficient good quality data we find that simple and direct relationships are usually most efficient in producing results. Not only are they easier to implement, they are also easier to explain and get buy-in. Some of these projects include:

Quantifying the effect on CTR of placing a hotel in different search results positions

Analysing and identifying changes in prices, automating the process into a price alerts feed for marketing purposes

Outside Work

tyeoh.com – very much a work in progress, exploring web app ideas that use data in innovative ways

tech.aaronteoh.com – this site, my latest project, to share what I’ve learnt and am working on in data, analytics, and the web

And then there’re the non web aspects of things I really enjoy, like exploring new places, playing the guitar, and hanging out with friends. Follow me on Instagram at @aaront_90 or on Facebook at @aaronteohofficial!

What’s Next?

I love where I am now, working on two of my favourite things, travel and technology, both on and off the job. I never imagined getting this deep into coding and statistics, having known absolutely nothing not too long ago. Having got this far, I hope to dig deeper, in making data analytics more approachable for all, and in advancing methods of data analysis. I hope you’ll join me in this journey, and we can both learn from each other.

Cheers to More Data Analytics!

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